Publication
Short Time Electricity Consumption Forecast in an Industry Facility
| dc.contributor.author | Ramos, Daniel | |
| dc.contributor.author | Faria, Pedro | |
| dc.contributor.author | Vale, Zita | |
| dc.contributor.author | Correia, Regina | |
| dc.date.accessioned | 2023-02-02T12:00:12Z | |
| dc.date.available | 2023-02-02T12:00:12Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The work in this article uses artificial neural networks and support vector machine to forecast electricity consumption in an industrial facility. The main objective is to show that such a problem should be treated with a contextual approach that identifies the most adequate technic in each moment for a single building, contrary to the previous works in the literature that compare the accuracy of each method for the complete data set representing aggregated loads. 72 different algorithms have been implemented and tested. After that, the three most suitable ones are selected in order to support the automated decisions of the best algorithm according to the context. In this way, the implemented methodology finds the best method for the prediction of each 5 min. It can be later used to update the production planning in the industrial facility. It also discussed the size of historical data and the most suitable learning parameters for each method. The case study includes test data for one week and more than one year of training data. | pt_PT |
| dc.description.sponsorship | This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project COLORS (PTDC/EEI-EEE/28967/2017). The work has also been done in the scope of projects UIDB/00760/2020, CEECIND/02887/2017, and SFRH/BD/144200/2019, financed by FEDER Funds through COMPETE program and from National Funds through (FCT). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1109/TIA.2021.3123103 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/22111 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.relation | COLORS - CONTEXTUAL LOAD FLEXIBILITY REMUNERATION STRATEGIES | |
| dc.relation | Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development | |
| dc.relation | Not Available | |
| dc.relation | Effective DR gathering and deployment for intensive renewable integration using aggregation and machine learning | |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9591379 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Demand Response | pt_PT |
| dc.subject | Load Shifting | pt_PT |
| dc.subject | Remuneration | pt_PT |
| dc.subject | Rebound Effect | pt_PT |
| dc.subject | Trustworthiness | pt_PT |
| dc.title | Short Time Electricity Consumption Forecast in an Industry Facility | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | COLORS - CONTEXTUAL LOAD FLEXIBILITY REMUNERATION STRATEGIES | |
| oaire.awardTitle | Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development | |
| oaire.awardTitle | Not Available | |
| oaire.awardTitle | Effective DR gathering and deployment for intensive renewable integration using aggregation and machine learning | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEEI-EEE%2F28967%2F2017/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/CEEC IND 2017/CEECIND%2F02887%2F2017%2FCP1417%2FCT0003/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F144200%2F2019/PT | |
| oaire.citation.endPage | 130 | pt_PT |
| oaire.citation.issue | 1 | pt_PT |
| oaire.citation.startPage | 123 | pt_PT |
| oaire.citation.title | IEEE Transactions on Industry Applications | pt_PT |
| oaire.citation.volume | 58 | pt_PT |
| oaire.fundingStream | 9471 - RIDTI | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | CEEC IND 2017 | |
| person.familyName | Faria | |
| person.familyName | Vale | |
| person.givenName | Pedro | |
| person.givenName | Zita | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | B212-2309-F9C3 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-5982-8342 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 7004115775 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | article | pt_PT |
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